DOCS . ULTRALYTICS . COM {
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Title:
Python Usage - Ultralytics YOLO Docs
Description:
Learn to integrate Ultralytics YOLO in Python for object detection, segmentation, and classification. Load and train models, and make predictions easily with our comprehensive guide.
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Keywords {🔍}
model, yolo, export, mode, train, ultralytics, results, python, import, load, models, val, benchmark, predict, object, detection, dataset, format, track, custom, performance, onnx, examples, training, datasets, usage, images, yoloyolonpt, trainer, modes, deployment, validate, pretrained, perform, validation, epochs, image, formats, predictions, set, yoloyolonyaml, provide, official, quickstart, tasks, customization, trainers, integrate, projects, segmentation,
Topics {✒️}
formats - image/dir/path/url/video/pil/ndarray support custom tasks top previous cli fine-tuning existing models simple export yolo models custom datasets real-time object tracking mastering ultralytics yolo integrating ultralytics yolo python usage exporting yolo models validating yolo models modes track python pil import image datasets export models customization yolo import detectionpredictor ultralytics yolo official yolo model benchmark model performance pretrained models val mode page benchmark examples predict mode section export mode documentation live video stream custom yolo model val val pretrained yolo model high-level wrapper machine learning workflows computer vision task train mode page optimal export format python project python projects python interface benchmarks provide information provide comprehensive functionalities trained yolo model easily customize trainers models dynamic batch-size usage //ultralytics ultralytics real-time
Questions {❓}
- Can I validate my YOLO model on different datasets?
- How can I integrate YOLO into my Python project for object detection?
- How do I export YOLO models for deployment?
- How do I train a custom YOLO model using my dataset?
- What are the different modes available in YOLO?
Schema {🗺️}
["Article","FAQPage"]:
context:https://schema.org
headline:Python
image:
https://img.youtube.com/vi/GsXGnb-A4Kc/maxresdefault.jpg
datePublished:2023-11-12 02:49:37 +0100
dateModified:2025-03-30 20:40:15 +0800
author:
type:Organization
name:Ultralytics
url:https://ultralytics.com/
abstract:Learn to integrate Ultralytics YOLO in Python for object detection, segmentation, and classification. Load and train models, and make predictions easily with our comprehensive guide.
mainEntity:
type:Question
name:How can I integrate YOLO into my Python project for object detection?
acceptedAnswer:
type:Answer
text:Integrating Ultralytics YOLO into your Python projects is simple. You can load a pretrained model or train a new model from scratch. Here's how to get started: See more detailed examples in our Predict Mode section.
type:Question
name:What are the different modes available in YOLO?
acceptedAnswer:
type:Answer
text:Ultralytics YOLO provides various modes to cater to different machine learning workflows. These include: Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.
type:Question
name:How do I train a custom YOLO model using my dataset?
acceptedAnswer:
type:Answer
text:To train a custom YOLO model, you need to specify your dataset and other hyperparameters. Here's a quick example: For more details on training and hyperlinks to example usage, visit our Train Mode page.
type:Question
name:How do I export YOLO models for deployment?
acceptedAnswer:
type:Answer
text:Exporting YOLO models in a format suitable for deployment is straightforward with the export function. For example, you can export a model to ONNX format: For various export options, refer to the Export Mode documentation.
type:Question
name:Can I validate my YOLO model on different datasets?
acceptedAnswer:
type:Answer
text:Yes, validating YOLO models on different datasets is possible. After training, you can use the validation mode to evaluate the performance: Check the Val Mode page for detailed examples and usage.
Organization:
name:Ultralytics
url:https://ultralytics.com/
Question:
name:How can I integrate YOLO into my Python project for object detection?
acceptedAnswer:
type:Answer
text:Integrating Ultralytics YOLO into your Python projects is simple. You can load a pretrained model or train a new model from scratch. Here's how to get started: See more detailed examples in our Predict Mode section.
name:What are the different modes available in YOLO?
acceptedAnswer:
type:Answer
text:Ultralytics YOLO provides various modes to cater to different machine learning workflows. These include: Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.
name:How do I train a custom YOLO model using my dataset?
acceptedAnswer:
type:Answer
text:To train a custom YOLO model, you need to specify your dataset and other hyperparameters. Here's a quick example: For more details on training and hyperlinks to example usage, visit our Train Mode page.
name:How do I export YOLO models for deployment?
acceptedAnswer:
type:Answer
text:Exporting YOLO models in a format suitable for deployment is straightforward with the export function. For example, you can export a model to ONNX format: For various export options, refer to the Export Mode documentation.
name:Can I validate my YOLO model on different datasets?
acceptedAnswer:
type:Answer
text:Yes, validating YOLO models on different datasets is possible. After training, you can use the validation mode to evaluate the performance: Check the Val Mode page for detailed examples and usage.
Answer:
text:Integrating Ultralytics YOLO into your Python projects is simple. You can load a pretrained model or train a new model from scratch. Here's how to get started: See more detailed examples in our Predict Mode section.
text:Ultralytics YOLO provides various modes to cater to different machine learning workflows. These include: Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.
text:To train a custom YOLO model, you need to specify your dataset and other hyperparameters. Here's a quick example: For more details on training and hyperlinks to example usage, visit our Train Mode page.
text:Exporting YOLO models in a format suitable for deployment is straightforward with the export function. For example, you can export a model to ONNX format: For various export options, refer to the Export Mode documentation.
text:Yes, validating YOLO models on different datasets is possible. After training, you can use the validation mode to evaluate the performance: Check the Val Mode page for detailed examples and usage.
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